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A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs

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Title:A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs
Authors:Weinzierl, Sven
Stierle, Matthias
Zilker, Sandra
Matzner, Martin
Keywords:Service Analytics
predictive process monitoring
process mining
recommender system
web usage mining
Date Issued:07 Jan 2020
Abstract:Software companies that offer web-based services instead of local installations can record the user’s interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs. We present a technique for a next click recommender system. Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks. We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context. The technique was evaluated with a real-life data set from a financial software provider. We used a hidden Markov model (HMM) as the baseline. The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Service Analytics

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